1 Questions

how many image files from lemnatec database?
how many image files did we expect from the database?
does the results file match the expected number of results?

2 Data Analysis

2.1 VIS Results

2.1.1 Analysis of Image Files

Imaging started on 2019-07-18 and ended on 2019-08-05. There were 0 days without an image for any sample.

The experiment was run for 19 days with 42 samples, and 3 image per day per sample, so we expected 2394 images. We have 1637 images. This is 68.4%. Hence we are missing 757 or 31.6% of images.

We expect 126 images per day:

2.1.2 Analysis of Results

Here we will read in the output file from our plantcv image analysis. Hopefully each image was analyzed and has a corresponding output.

output = read_csv(here::here('output','vis','vis.csv-single-value-traits.csv'),
                  na = 'NA') %>% 
  mutate(jobdate = as.Date(timestamp))

gmap = read_csv('data/genotype_map.csv')

output <- full_join(output,gmap, by = c('plantbarcode' = 'plantbarcode', 'roi'))

We expected 252 data points per day.

This graph shows the number of entries on each date in the output file. This includes NA values.

We can check to see if we lost any output data compared to the input. For example, if a plant died then we’d see a step drop in the number of samples. The differences on each day between the graph above and the graph below indicates the number of lost plants.

2.1.2.1 Replication

Do the datapoints from the different replicates look stable on each day?

Before we look at replication, lets see how many samples were flagged for non-unique roi or out of frame plants.

We need to test if all the ROI are present and distinct. If the plants merged together, then the plant area will be the same for both roi of a plantbarcode. If 1 plant died then there should only be 1 roi for a plantbarcode.

Table: Sample IDs with missing ROI (shown in black below)

Table: Sample IDs with nondistinct ROI (shown in red below)

0 data points with multiple nondistinct roi will be removed from the dataset and saved as level1 product. Please use the level1 output for further analysis.

2.2 PSII Results

2.2.1 Analysis of Image Files

Imaging started on 2019-07-18 and ended on 2019-08-05. There were 0 days without an image for any sample.

The experiment was run for 19 days with 42 samples, and 36 image per day per sample, so we expected 28728 images. We have 23283 images. This is 81.0%. Hence we are missing 5445 or 19.0% of images.

We expect 1512 images per day:

2.2.2 Analysis of Results

Here we will read in the output file from our plantcv image analysis. Hopefully each image was analyzed and has a corresponding output.

n_param = 16 #there are n photosynthetic parameters -->

output = read_csv('output/psII/output_psII_level0.csv',
                  na = 'nan') %>%
  mutate(jobdate = as.Date(jobdate),
         parameter = forcats::as_factor(parameter)) %>% 
  rename(plantbarcode = sampleid)

We expect 2688 datapoints per day.

We can check to see if we lost any output data compared to the input. For example, if a plant died then we’d see the # of datapoints decrease over time.

We expect 224 datapoints per day per genotype.

2.2.2.1 Replication